{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-28T14:41:08.555851Z",
     "start_time": "2018-08-28T14:41:07.897378Z"
    }
   },
   "outputs": [],
   "source": [
    "import random as rn\n",
    "import os\n",
    "import numpy as np\n",
    "os.environ['PYTHONHASHSEED'] = '0'\n",
    "np.random.seed(42)\n",
    "rn.seed(123)\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from scipy.stats import randint as sp_randint\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.model_selection import PredefinedSplit\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import uniform\n",
    "from sklearn.metrics import make_scorer\n",
    "pd.set_option('display.max_columns', 50)\n",
    "pd.set_option('display.max_rows', 100)\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "Path = 'D:\\\\APViaML'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-28T14:41:09.128397Z",
     "start_time": "2018-08-28T14:41:08.570896Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_demo_dict_data():\n",
    "    file = open(Path + '\\\\data\\\\alldata_demo_top1000.pkl','rb')\n",
    "    raw_data = pickle.load(file)\n",
    "    file.close()\n",
    "    return raw_data\n",
    "data = get_demo_dict_data()\n",
    "top_1000_data_X = data['X']\n",
    "top_1000_data_Y = data['Y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-28T14:41:14.995941Z",
     "start_time": "2018-08-28T14:41:14.989928Z"
    }
   },
   "outputs": [],
   "source": [
    "def Evaluation_fun(predict_array,real_array):\n",
    "    List1 = []\n",
    "    List2 = []\n",
    "    if len(predict_array) != len(real_array):\n",
    "        print('Something is worng!')\n",
    "    else:\n",
    "        for i in range(len(predict_array)):\n",
    "            List1.append(np.square(predict_array[i]-real_array[i]))\n",
    "            List2.append(np.square(real_array[i]))\n",
    "        result = round(100*(1 - sum(List1)/sum(List2)),3)\n",
    "    return result\n",
    "\n",
    "def my_custom_score_func(ground_truth, predictions):\n",
    "    \n",
    "    num1 =sum((ground_truth-predictions)**2)\n",
    "    num2 = sum(ground_truth**2)\n",
    "    return 1-num1/num2\n",
    "\n",
    "self_score = make_scorer(my_custom_score_func, greater_is_better=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-28T14:45:36.704640Z",
     "start_time": "2018-08-28T14:45:36.692608Z"
    }
   },
   "outputs": [],
   "source": [
    "def rolling_model_GBRT_annual(\n",
    "    df_X ,\n",
    "    df_Y ,\n",
    "    num1 = 100):\n",
    "                                    \n",
    "    y_predict_list = []\n",
    "    test_performance_score_list = []\n",
    "    best_features = []\n",
    "\n",
    "    for i in range(30):\n",
    "        print(i)\n",
    "        ## define data index\n",
    "        traindata_startyear_str = str(1958) \n",
    "        traindata_endyear_str = str(i + 1987) \n",
    "        vdata_startyear_str = str(i + 1976) \n",
    "        vdata_endyear_str = str(i + 1987) \n",
    "        testdata_startyear_str = str(i + 1988) \n",
    " \n",
    "\n",
    "        ## get data     \n",
    "        X_traindata =  np.array(df_X.loc[traindata_startyear_str:traindata_endyear_str])\n",
    "        Y_traindata = np.array(df_Y.loc[traindata_startyear_str:traindata_endyear_str])\n",
    "        X_vdata = np.array(df_X.loc[vdata_startyear_str:vdata_endyear_str])\n",
    "        Y_vdata = np.array(df_Y.loc[vdata_startyear_str:vdata_endyear_str])\n",
    "        X_testdata = np.array(df_X.loc[testdata_startyear_str])\n",
    "        Y_testdata = np.array(df_Y.loc[testdata_startyear_str])\n",
    "\n",
    "        num_valid_size = len(X_traindata)-len(X_vdata)\n",
    "        \n",
    "        test_fold = -1 * np.ones(len(X_traindata))\n",
    "        test_fold[num_valid_size:] = 0\n",
    "        ps = PredefinedSplit(test_fold)\n",
    "        \n",
    "        # specify parameters and distributions to sample from\n",
    "        \n",
    "        \n",
    "        param_dist ={   \"max_features\": sp_randint(5, 100),\n",
    "                        \"max_depth\": sp_randint(3, 12),\n",
    "                        \"min_samples_split\": sp_randint(100,1000),\n",
    "                        \"min_samples_leaf\": sp_randint(100,1000),\n",
    "                        \"n_estimators\":sp_randint(5, 100),\n",
    "                     \"learning_rate\":uniform(0.001, 0.1),\n",
    "                     \"subsample\":uniform(0.6, 0.4)\n",
    "                      }\n",
    "\n",
    "        clf_GBRT = GradientBoostingRegressor(loss='huber',random_state=100)\n",
    "\n",
    "        \n",
    "        # run randomized search\n",
    "        n_iter_search = num1\n",
    "        estim = RandomizedSearchCV(clf_GBRT, param_distributions=param_dist,\n",
    "                                           n_iter=n_iter_search,scoring=self_score,\n",
    "                                          cv=ps.split(),iid=False,random_state=100)\n",
    "                      \n",
    "        estim.fit(X_traindata, Y_traindata)\n",
    "        \n",
    "        temp_best_features = estim.best_params_['max_features']\n",
    "        best_features.append(temp_best_features)\n",
    "        \n",
    "        best_estimator = estim.best_estimator_\n",
    "        estim = best_estimator.fit(X_traindata[:num_valid_size], Y_traindata[:num_valid_size])\n",
    "        \n",
    "        file = open(Path + '\\\\model\\\\GBRT\\\\' + testdata_startyear_str+ 'Model_GBRT_Top1000_Prediction.pkl', 'wb')\n",
    "        pickle.dump(estim, file)\n",
    "        file.close()\n",
    "\n",
    "        ## model testing\n",
    "         \n",
    "        test_pre_y_arry = estim.predict(X_testdata)\n",
    "        y_predict_list1=[]\n",
    "        for x in test_pre_y_arry:\n",
    "            y_predict_list.append(x)\n",
    "            y_predict_list1.append(x)        \n",
    "        test_performance_score =  Evaluation_fun(y_predict_list1,Y_testdata )\n",
    "        test_performance_score_list.append(test_performance_score)\n",
    "        \n",
    "    return y_predict_list,test_performance_score_list,best_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-29T09:26:27.828584Z",
     "start_time": "2018-08-28T14:52:34.701736Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
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      "28\n",
      "29\n"
     ]
    }
   ],
   "source": [
    "y_predict_list,test_performance_score_list,best_feature_list = rolling_model_GBRT_annual(df_X = top_1000_data_X,df_Y = top_1000_data_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-29T11:10:36.722877Z",
     "start_time": "2018-08-29T11:10:36.717870Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.541766666666667\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(test_performance_score_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-29T11:10:37.866919Z",
     "start_time": "2018-08-29T11:10:37.594195Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.017"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_real = np.array(top_1000_data_Y.loc['1988':])\n",
    "Evaluation_fun(y_predict_list,y_real)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-29T11:10:40.994235Z",
     "start_time": "2018-08-29T11:10:40.843835Z"
    }
   },
   "outputs": [],
   "source": [
    "file = open(Path + '\\\\output\\\\data\\\\Model_GBRT_Top1000_Prediction.pkl', 'wb')\n",
    "pickle.dump(y_predict_list, file)\n",
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-29T11:11:13.882751Z",
     "start_time": "2018-08-29T11:11:13.877739Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9,\n",
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       " 54,\n",
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       " 27,\n",
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       " 85,\n",
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       " 97,\n",
       " 60,\n",
       " 42,\n",
       " 90,\n",
       " 64]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_feature_list "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-29T11:18:42.224229Z",
     "start_time": "2018-08-29T11:18:42.218213Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_plot_figure(X_fig,Y_fig):\n",
    "    plt.figure(figsize=(16,8))\n",
    "    plt.plot(X_fig,Y_fig,\n",
    "              linestyle = '-', \n",
    "              linewidth = 2, \n",
    "              color = '#ff9999', \n",
    "              marker = 'o', \n",
    "              markersize = 6, \n",
    "              markeredgecolor='black', \n",
    "              markerfacecolor='#ff9999' )\n",
    "    plt.ylabel('Tree Depth')\n",
    "    plt.xlabel('Year')\n",
    "    plt.grid(True,ls='--')\n",
    "    plt.title('GBRT+H')\n",
    "    ax = plt.gca()\n",
    "    ax.spines['top'].set_visible(False) #去掉上边框\n",
    "    ax.spines['right'].set_visible(False) #去掉右边框\n",
    "    plt.savefig(Path + '\\\\output\\\\figure\\\\fig3_GBRT.jpg',bbox_inches = 'tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-08-29T11:18:43.710181Z",
     "start_time": "2018-08-29T11:18:43.002298Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_fig = range(1988,2018)\n",
    "get_plot_figure(X_fig,best_feature_list )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
